UNDERSTANDING THE NEW U.S. NATIONAL SPACE POLICY，John M. Logsdon Director, Space Policy Institute Elliott School of International Affairs The George Washington University Washington, DC, USA
標簽： U.S. UNDERSTANDING NATIONAL POLICY
上傳時間： 2014-12-20
上傳用戶：蟲蟲蟲蟲蟲蟲
THE UNITED STATES AND HUMAN SPACE EXPLORATION，John M. Logsdon Director, Space Policy Institute Elliott School of International Affairs The George Washington University Washington, DC, USA 在北大的講座資料
標簽： EXPLORATION STATES UNITED HUMAN
上傳時間： 2016-03-06
上傳用戶：zycidjl
/*************************************************************************************************** The 4×4-keyboard drivers *COMPANY NAME: WUYI University *MODULE NAME： Keyboard drivers *WRITTEN BY： Pang Weicong *FUNCTION DESCRIPTION： Keyboard input processing *EDITION： The first edition V1.0 *DATE： 2007-04-16 *Copyright： (c)2007 Pang Weicong **************************************************************************************************/
標簽：
上傳時間： 2014-01-05
上傳用戶：xiaodu1124
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
The second volume in the Write Great Code series supplies the critical information that today s computer science students don t often get from college and University courses: How to carefully choose their high-level language statements to produce efficient code. Write Great Code, Volume 2: Thinking Low-Level, Writing High-Level, teaches software engineers how compilers translate high-level language statements and data structures into machine code. Armed with this knowledge, a software engineer can make an informed choice concerning the use of those high-level structures to help the compiler produce far better machine code--all without having to give up the productivity and portability benefits of using a high-level language
標簽： information the critical supplies
上傳時間： 2014-02-21
上傳用戶：luke5347
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：康郎
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
標簽： algorithms problems Several trivial
上傳時間： 2014-01-20
上傳用戶：royzhangsz
FreeTTS is a speech synthesis system written entirely in the Java programming language. It is based upon Flite, a small, fast, run-time speech synthesis engine, which in turn is based upon University of Edinburgh s Festival Speech Synthesis System and Carnegie Mellon University s FestVox project.
標簽： programming synthesis entirely language
上傳時間： 2014-08-29
上傳用戶：cylnpy
對于信號的奇異性檢測，也是一個道理。 ％ 小波變換用于奇異檢測 ％ 編程人: 沙威(Wei Sha) 安徽大學(Anhui University) ws108@ahu.edu.cn
上傳時間： 2014-01-06
上傳用戶：我干你啊
